library(tidyverse)
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library(readxl)
library(rvest)
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## guess_encoding
library(httr)
library(lubridate)
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library(flexdashboard)
library(plotly)
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Import dataset
raw_sub_crime =
read_csv("./data/subwaycrime.csv") %>%
janitor::clean_names()
## New names:
## * `` -> ...1
## Warning: One or more parsing issues, see `problems()` for details
## Rows: 6244 Columns: 37
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## Delimiter: ","
## chr (20): BORO_NM, CMPLNT_FR_DT, CMPLNT_TO_DT, CRM_ATPT_CPTD_CD, JURIS_DESC...
## dbl (11): ...1, CMPLNT_NUM, ADDR_PCT_CD, JURISDICTION_CODE, KY_CD, PD_CD, T...
## lgl (4): HADEVELOPT, HOUSING_PSA, LOC_OF_OCCUR_DESC, PARKS_NM
## time (2): CMPLNT_FR_TM, CMPLNT_TO_TM
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
raw_sub_station =
read_xlsx("./data/subway_info_final.xlsx") %>%
janitor::clean_names()
Crime event v.s. Month
sub_crime_freq =
raw_sub_crime %>%
select(cmplnt_fr_dt, cmplnt_fr_tm, ofns_desc, law_cat_cd, station_name, latitude, longitude) %>%
rename("date" = "cmplnt_fr_dt", "time" = "cmplnt_fr_tm", "crime_event" = "ofns_desc") %>%
mutate(date = as.Date(date, "%m/%d/%Y")) %>%
mutate(date = substring(as.character(as.Date(date, "%m/%d/%y")),1,7)) %>%
filter(!(date %in% c("1971-09","2016-11","2018-04","2018-05")))
plot_1 =
sub_crime_freq %>%
group_by(date) %>%
summarise(event_num = n()) %>%
plot_ly(
x = ~date, y = ~event_num, type = "scatter", mode = "markers"
)
layout(plot_1, title = "Crime events over time", xaxis = list(title = "Month"), yaxis = list(title = "Number of Crime Events"))
Generally, Top 10 most frequent crime events
sub_crime_freq %>%
count(crime_event) %>%
mutate(crime_event = fct_reorder(crime_event, n)) %>%
slice_max(n, n = 10) %>%
plot_ly(x = ~ crime_event, y = ~ n, color = ~ crime_event, type = "bar", colors = "viridis") %>%
layout(yaxis = list(title = 'Top 10 most frequenct crime events'),
xaxis = list(title = 'Crime Events'))
Top 5 Most Frequent crime events change over time
most_freq_event =
sub_crime_freq %>%
group_by(crime_event) %>%
summarize(event_num = n()) %>%
arrange(desc(event_num))
plot_2 =
sub_crime_freq %>%
group_by(law_cat_cd, date) %>%
summarize(event_num = n()) %>%
ggplot(aes(x = date, y = event_num, color = law_cat_cd)) +
geom_point() + geom_line() +
theme(legend.text=element_text(size=7))
## `summarise()` has grouped output by 'law_cat_cd'. You can override using the `.groups` argument.
ggplotly(plot_2) %>%
layout(plot_2, title = "3 Degrees of Crime Events Numbers Over Time", xaxis = list(title = "Month"), yaxis = list(title = "Number of Crime Events"))
Crime events v.s. Time
sub_crime_time =
raw_sub_crime %>%
select(cmplnt_fr_dt, cmplnt_fr_tm, ofns_desc, station_name, latitude, longitude) %>%
rename("start_date" = "cmplnt_fr_dt", "start_time" = "cmplnt_fr_tm", "crime_event" = "ofns_desc") %>%
mutate(
event_time = as.character(case_when(
hms("00:00:00") <= start_time & start_time < hms("02:00:00") ~hms("00:00:00"),
hms("02:00:00") <= start_time & start_time < hms("04:00:00") ~hms("04:00:00"),
hms("04:00:00") <= start_time & start_time < hms("06:00:00") ~hms("04:00:00"),
hms("06:00:00") <= start_time & start_time < hms("08:00:00") ~hms("08:00:00"),
hms("08:00:00") <= start_time & start_time < hms("10:00:00") ~hms("08:00:00"),
hms("10:00:00") <= start_time & start_time < hms("12:00:00") ~hms("12:00:00"),
hms("12:00:00") <= start_time & start_time < hms("14:00:00") ~hms("12:00:00"),
hms("14:00:00") <= start_time & start_time < hms("16:00:00") ~hms("16:00:00"),
hms("16:00:00") <= start_time & start_time < hms("18:00:00") ~hms("16:00:00"),
hms("18:00:00") <= start_time & start_time < hms("20:00:00") ~hms("20:00:00"),
hms("20:00:00") <= start_time & start_time < hms("23:59:59") ~hms("20:00:00"),
))
) %>%
filter(crime_event %in% c("CRIMINAL MISCHIEF & RELATED OF", "ASSAULT 3 & RELATED OFFENSES","HARRASSMENT 2","GRAND LARCENY","DANGEROUS DRUGS","FELONY ASSAULT","ROBBERY","PETIT LARCENY","FORGERY","SEX CRIMES","OFF. AGNST PUB ORD SENSBLTY &","DANGEROUS WEAPONS"))
plot_3 =
sub_crime_time %>%
mutate(event_time = as.factor(event_time)) %>%
ggplot(aes(x = event_time %>% fct_infreq(), fill = crime_event)) +
geom_histogram(stat = "count", width = 0.9, height = 2) +
labs(
title = "Frequency of crime events v.s. Time points",
x = "Occurrence time",
y = "Frequency of crime events") +
theme_bw() +
theme(
plot.title = element_text(hjust = 1),
legend.position = "bottom",
legend.text = element_text(size = 8)) +
guides(col = guide_legend(nrow = 2))
## Warning: Ignoring unknown parameters: binwidth, bins, pad, height
ggplotly(plot_3) %>%
layout(legend = list(
orientation = "h",
xanchor = "center",
yanchor = "top",
x = 0.3,
y = - 0.3
)
)
Response time
crime_response_time =
raw_sub_crime %>%
rename("start_date" = "cmplnt_fr_dt", "start_time" = "cmplnt_fr_tm", "end_date" = "cmplnt_to_dt", "end_time" = "cmplnt_to_tm", "crime_event" = "ofns_desc") %>%
select(start_date, start_time, end_date, end_time, crime_event, law_cat_cd) %>%
drop_na(start_time, end_time) %>%
filter(crime_event %in% c("CRIMINAL MISCHIEF & RELATED OF", "ASSAULT 3 & RELATED OFFENSES","HARRASSMENT 2","GRAND LARCENY","DANGEROUS DRUGS","FELONY ASSAULT","ROBBERY","PETIT LARCENY","FORGERY","SEX CRIMES","OFF. AGNST PUB ORD SENSBLTY &","DANGEROUS WEAPONS")) %>%
mutate(start_date = as.character(as.Date(start_date, "%m/%d/%Y")),
end_date = as.character(as.Date(end_date, "%m/%d/%Y"))) %>%
mutate(start = as.POSIXct(paste(start_date, start_time), format = "%Y-%m-%d %H:%M:%S"),
end = as.POSIXct(paste(end_date, end_time), format = "%Y-%m-%d %H:%M:%S")) %>%
mutate(response_time = difftime(end, start, units = "hours")) %>%
mutate(date = substring(start_date,1,7)) %>%
filter(!(start_date %in% c("1971-09","2016-11","2018-04","2018-05"))) %>%
mutate(
event_time = as.character(case_when(
hms("00:00:00") <= start_time & start_time < hms("02:00:00") ~hms("00:00:00"),
hms("02:00:00") <= start_time & start_time < hms("04:00:00") ~hms("04:00:00"),
hms("04:00:00") <= start_time & start_time < hms("06:00:00") ~hms("04:00:00"),
hms("06:00:00") <= start_time & start_time < hms("08:00:00") ~hms("08:00:00"),
hms("08:00:00") <= start_time & start_time < hms("10:00:00") ~hms("08:00:00"),
hms("10:00:00") <= start_time & start_time < hms("12:00:00") ~hms("12:00:00"),
hms("12:00:00") <= start_time & start_time < hms("14:00:00") ~hms("12:00:00"),
hms("14:00:00") <= start_time & start_time < hms("16:00:00") ~hms("16:00:00"),
hms("16:00:00") <= start_time & start_time < hms("18:00:00") ~hms("16:00:00"),
hms("18:00:00") <= start_time & start_time < hms("20:00:00") ~hms("20:00:00"),
hms("20:00:00") <= start_time & start_time < hms("23:59:59") ~hms("20:00:00"),
))
)
crime_response_time %>%
mutate(crime_event = fct_reorder(crime_event, response_time)) %>%
plot_ly(y = ~ response_time, color = ~ crime_event, type = "box", colors = "viridis")